Utilizing Data Analytics for Decision Support System of St. Paul University Philippines' ICT Services

This study presents the design, development, and evaluation of a Data Analytics-based Decision Support System for ICT services at St. Paul University Philippines. The study aims to enhance ICT service management by utilizing descriptive, diagnostic, predictive, and prescriptive analytics to optimize decision-making, improve service efficiency, and address existing challenges such as equipment maintenance, service request management, and system performance monitoring. Using a Descriptive and Developmental Research Design, the study evaluates the DSS through both ISO/IEC 25010 software quality standards and the Technology Acceptance Model. IT experts assess the system’s functionality, usability, reliability, and maintainability, while SPUP administrators evaluate its perceived ease of use, usefulness, and adoption potential. Data sources include historical and real-time service request logs, equipment maintenance records, and structured survey responses. Thematic analysis is also employed to identify key challenges in the ICT HelpDesk System. The SCRUM methodology is adopted for system development, ensuring iterative improvements through continuous stakeholder feedback. The DSS integrates advanced analytical models, including ARIMA for forecasting ICT service demands and equipment failures, clustering algorithms for diagnostic insights, and rule-based logic for prescriptive analytics. Findings from the study demonstrate that the DSS enhances decision-making efficiency by providing actionable insights, improving service response times, and supporting proactive maintenance strategies. The results contribute to ICT service optimization at SPUP, offering a scalable framework for data-driven decision-making in higher education institutions.